AI Engineer

• Design and build internal AI solutions that improve engineering productivity and software delivery
• Develop AI-powered developer workflows and assistants, agentic SDLC automation patterns, and internal tools for code analysis, documentation, testing, migration, and engineering support
• Create reusable prompt, tool-calling, workflow, and reference implementation patterns
• Build multi-step agentic workflows, tool-calling and orchestration patterns, RAG-based internal knowledge solutions, shared SDKs, templates, integration examples, and reusable components for copilots and agents
• Partner with senior architects and engineering leaders to establish scalable patterns
• Work directly with engineering teams, champions, and internal stakeholders to support AI adoption
• Pair with teams on AI use cases and implementation patterns
• Provide technical guidance on LLM, RAG, and agentic workflow design
• Support proof-of-concept efforts and mature them into repeatable practices
• Create playbooks, examples, templates, and documentation for internal engineering use
• Participate in office hours, workshops, and AI enablement sessions
• Help define and apply evaluation and governance practices for prompt and workflow evaluation, accuracy, relevance, usefulness, safety, responsible AI, PII handling, logging, observability, feedback loops, and human-in-the-loop review
• Partner with engineering leadership, product teams, architecture, security, and other stakeholders to identify and deliver high-impact AI use cases
• Translate engineering productivity needs into AI-enabled solutions and support roadmap-aligned internal AI initiatives
• Contribute to adoption and capacity-improvement goals and help measure AI enablement impact
• Communicate technical concepts clearly to engineering and non-engineering audiences
• Design AI solutions with model selection and routing, prompt and context optimization, caching and retrieval efficiency, latency, reliability, build-vs-buy, and vendor lock-in considerations

AI Developer IV

• Develop and maintain software applications, dashboards, APIs, and automation tools supporting manufacturing operations
• Assist with AI-enabled solutions including Generative AI applications, copilots, intelligent assistants, and analytics tools
• Create and maintain SQL queries, reports, and data integrations across manufacturing and business systems
• Support development of data pipelines and data processing workflows
• Participate in application testing, deployment, and troubleshooting
• Collaborate with engineers and business stakeholders to understand requirements and deliver software solutions
• Develop reports and visualizations using Power BI and other analytics platforms
• Support cloud-based applications and services under guidance from senior engineers
• Follow software development best practices, version control processes, and security standards
• Investigate and resolve application, data integration, and reporting issues
• Document solutions, processes, and technical implementations

Manufacturing Data, AI Developer

• Develop and maintain software applications, dashboards, APIs, and automation tools supporting manufacturing operations
• Assist with AI-enabled solutions such as Generative AI applications, copilots, intelligent assistants, and analytics tools
• Create and maintain SQL queries, reports, and data integrations across manufacturing and business systems
• Support development of data pipelines and data processing workflows
• Participate in application testing, deployment, and troubleshooting
• Collaborate with engineers and business stakeholders to understand requirements and deliver software solutions
• Develop reports and visualizations using Power BI and other analytics platforms
• Support cloud-based applications and services under guidance from senior engineers
• Follow software development best practices, version control processes, and security standards
• Investigate and resolve issues related to applications, data integrations, and reporting solutions
• Document solutions, processes, and technical implementations

• Support governed data pipelines, including Snowflake-enabled datasets, by preparing, cleaning, validating, and connecting engineering, compliance, manufacturing, service, warranty, and field-quality data
• Assist with SQL queries, data models, metadata fields, and data-quality checks
• Contribute to AI-agent implementation by configuring workflows, retrieval patterns, prompt examples, test cases, and deployment-support materials
• Prepare standards, process guidance, historical examples, compliance references, investigation learnings, and engineering knowledge content for AI-assisted workflows and evaluation datasets
• Help test, validate, and deploy AI-agent capabilities using enterprise platforms, Snowflake-enabled data assets, Microsoft 365 Copilot, Copilot Studio, APIs, and related tools
• Capture data-quality issues, manual handoffs, duplicated steps, user pain points, pilot feedback, and improvement ideas in issue-tracking or backlog tools
• Support analysis of connected engineering, compliance, investigation, manufacturing, service, warranty, and field data to improve risk assessment, product-quality decisions, corrective-action follow-up, and service diagnostics
• Measure AI-agent output quality, efficiency, token usage, user feedback, and accuracy through evaluation datasets, regression testing, grounding checks, stress testing, and hallucination-reduction reviews
• Maintain implementation notes, prompt/configuration change logs, user guidance, training aids, data definitions, known limitations, and adoption content in Confluence, SharePoint, and similar platforms
• Collaborate with engineering, compliance, validation, manufacturing, service, quality, IT, defect investigation, and regional/global stakeholders on user acceptance testing, adoption, and governed AI/data solutions

Applied AI Engineer

• Support governed data pipelines, including Snowflake-enabled datasets, by preparing, cleaning, validating, and connecting engineering, manufacturing, service, warranty, compliance, defect, and field-quality data
• Assist with SQL queries, data models, metadata fields, and data-quality checks
• Contribute to AI-agent implementation by configuring workflows, retrieval patterns, prompt examples, test cases, and deployment-support materials
• Prepare standards, process guidance, historical examples, compliance references, investigation learnings, and engineering knowledge for AI-assisted workflows and evaluation datasets
• Help test, validate, and deploy AI-agent capabilities using enterprise platforms, Snowflake-enabled data assets, Microsoft 365 Copilot, Copilot Studio, APIs, and related tools
• Capture data-quality issues, manual handoffs, duplicated steps, user pain points, pilot feedback, and improvement ideas in issue-tracking or backlog tools
• Support analysis of engineering, compliance, investigation, manufacturing, service, warranty, and field data to improve risk assessment, product-quality decisions, corrective-action follow-up, and service diagnostics
• Measure AI-agent output quality, efficiency, token usage, user feedback, and accuracy through evaluation datasets, regression testing, grounding checks, stress testing, and hallucination-reduction reviews
• Create and maintain implementation notes, prompt/configuration change logs, user guidance, training aids, data definitions, known limitations, and adoption content in Confluence, SharePoint, and similar platforms
• Collaborate with engineering, compliance, validation, manufacturing, service, quality, IT, defect investigation, and regional/global stakeholders on user acceptance testing, adoption, and governed AI/data solutions

AI Engineer jobs in Oregon

AI Developer IV

Manufacturing Data, AI Developer

Manufacturing Data, AI Developer

Applied AI Engineer

Applied AI Engineer, I